The Zika epidemic and abortion in Latin America: a scoping review
Bibliographic record
Abstract
BACKGROUND: Latin America presently has the world's highest burden of Zika virus, but there are unexplained differences in national rates of congenital malformations collectively referred to as Congenital Zika Syndrome (CZS) in the region. While Zika virulence and case detection likely contribute to these differences, policy-related factors, including access to abortion, may play important roles. Our goal was to assess perspectives on, and access to, abortion in Latin America in the context of the Zika epidemic. METHODS: We conducted a scoping review of peer-reviewed and gray literature published between January 2015 and December 2016, written in English, Spanish, Portuguese, or French. We searched PubMed, Scielo, and Google Scholar for literature on Zika and/or CZS and abortion, and used automated and manual review methods to synthesize the existing information. RESULTS: 36 publications met our inclusion criteria, the majority of which were qualitative. Publications were generally in favor of increased access to safe abortion as a policy-level response for mitigating the impact of CZS, but issues with implementation were cited as the main challenge. Aside from the reform of abortion regulation in Colombia, we did not find evidence that the Zika epidemic had triggered shifts in abortion policy in other countries. CONCLUSION: Abortion policy in the region remained largely unchanged following the Zika epidemic. Further empirical research on abortion access and differential rates of CZS across Latin American countries is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".